基于扰动观测器的工业机器人高精度闭环鲁棒控制

doi:10.3901/JME.2022.14.062

机械工程学报 ›› 2022, Vol. 58 ›› Issue (14): 62-70.doi: 10.3901/JME.2022.14.062

• 特邀专栏：大型构件视觉测量与机器人加工 • 上一篇下一篇

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基于扰动观测器的工业机器人高精度闭环鲁棒控制

张泽坤^1,2, 国凯^1,2, 孙杰^1,2

1. 山东大学机械工程学院济南 250061;
2. 教育部高效洁净机械制造重点实验室济南 250061

收稿日期:2021-06-06 修回日期:2022-02-28 出版日期:2022-07-20 发布日期:2022-09-07
通讯作者: 国凯(通信作者)，男，1990年出生，博士，教授，博士研究生导师。主要研究方向为智能机器人系统设计与控制。E-mail：kaiguo@sdu.edu.cn
作者简介:张泽坤，男，1997年出生，博士研究生。主要研究方向为鲁棒控制与自适应控制。E-mail：zekun@mail.sdu.edu.cn
基金资助:
国家自然科学基金(51975335)和山东省重点研发计划(2019GGX104008，2019JZZY020318)资助项目。

High-precision Closed-loop Robust Control of Industrial Robots Based on Disturbance Observer

ZHANG Zekun^1,2, GUO Kai^1,2, SUN Jie^1,2

1. School of Mechanical Engineering, Shandong University, Jinan 250061;
2. Key Laboratory of High-efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061

Received:2021-06-06 Revised:2022-02-28 Online:2022-07-20 Published:2022-09-07

摘要/Abstract

摘要： 工业机器人具有很高的重复定位精度但绝对定位精度较低，这限制了其在高精度加工中的应用。提高绝对定位精度的传统方法有几何参数标定和离线误差补偿，但它们的绝对定位误差仍有数百微米，往往不能满足要求。更有效的绝对定位精度提升手段是在线补偿，但目前相关研究大多基于简单的PID控制，难以在复杂工况下实现精确的轨迹跟踪。因此提出一种高精度鲁棒控制方法以提高工业机器人在线补偿中的位置控制精度和对于外界扰动的抵抗能力。该方法使用激光跟踪仪实时测量机器人末端位置，通过二阶扰动观测器识别外部扰动，并在扰动观测的基础上以滑模控制器完成机器人的鲁棒控制，其中扰动观测器观测误差的有界性和闭环系统的渐进稳定性均通过李雅普诺夫方法得到证明。该方法在COMAU工业机器人上得到验证，试验结果表明使用基于扰动观测器的闭环鲁棒控制进行在线补偿时，笛卡儿空间内追踪误差模值的方均根值为0.037 mm，仅为基于PID在线补偿方法的39%。

关键词: 工业机器人, 在线补偿, 扰动观测器, 滑模控制

Abstract: Industrial robots have high repeatable positioning accuracy but their absolute positioning accuracy is relatively low, which limits their application in high-precision machining. Traditional methods to improve absolute positioning accuracy include geometric parameter calibration and off-line error compensation, but their absolute positioning error is hundreds of microns and cannot meet the requirements. Online compensation is a more effective method, but most of the relevant researches are based on simple PID control, which is difficult to achieve accurate trajectory tracking in complex conditions. Thus, a high-precision robust control method is proposed to improve the control precision and robustness in online compensation. The method uses a laser tracker to measure the robot's terminal position in real time and identifies the external disturbance through a second-order disturbance observer. On this basis, a sliding mode controller is designed to complete the robot's robust control. The boundness of observation error of disturbance observer and the asymptotic stability of closed-loop system are proved by Lyapunov method. The proposed method is validated on a COMAU robot, experimental results show that the root-mean-square value of tracking error module in Cartesian space is 0.037 mm when the proposed control method is used for online compensation, which is only 39% of PID based online compensation method.

Key words: industrial robot, online compensation, disturbance observer, sliding mode control

中图分类号:

TP242

张泽坤, 国凯, 孙杰. 基于扰动观测器的工业机器人高精度闭环鲁棒控制[J]. 机械工程学报, 2022, 58(14): 62-70.

ZHANG Zekun, GUO Kai, SUN Jie. High-precision Closed-loop Robust Control of Industrial Robots Based on Disturbance Observer[J]. Journal of Mechanical Engineering, 2022, 58(14): 62-70.

参考文献

[1] KIM S H, NAM E, HA T I, et al. Robotic machining:A review of recent progress[J]. International Journal of Precision Engineering and Manufacturing, 2019, 20(9):1629-1642.
[2] 文科,张加波,乐毅,等.数控驱动的移动铣削机器人精度提升方法[J].机械工程学报, 2021, 57(5):72-80. WEN Ke. ZHANG Jiabo, YUE Yi, et al. Method for improving accuracy of NC-driven mobile milling robot[J]. Journal of Mechanical Engineering, 2021, 57(5):72-80.
[3] FROMMKNECHT A, KUEHNLE J, EFFENBERGER I, et al. Multi-sensor measurement system for robotic drilling[J]. Robotics and Computer-Integrated Manufacturing, 2017, 47(1):4-10.
[4] 李文龙,谢核,尹周平,等.机器人加工几何误差建模研究Ⅰ:空间运动链与误差传递[J].机械工程学报, 2021, 57(7):154-168. LI Wenlong, XIE He, YIN Zhouping, et al. The research of geometric error modeling of robotic machining I:Spatial motion chain and error transmission[J]. Journal of Mechanical Engineering, 2021, 57(7):154-168.
[5] JANEZ G, TIMI K, KARL G, et al. Accuracy improvement of robotic machining based on robot's structural properties[J]. The International Journal of Advanced Manufacturing Technology, 2020, 108(5):1309-1329.
[6] ZHONG Xiaolin, LEWIS J M, FRANCIS L N N. Autonomous robot calibration using a trigger probe[J]. Robotics and Autonomous Systems, 1996, 18(4):395-410.
[7] LARRANZI L, CRISRALLI C, MASSA D, et al. Geometrical calibration of a 6-axis robotic arm for high accuracy manufacturing task[J]. The International Journal of Advanced Manufacturing Technology, 2020, 111(7):1813-1829.
[8] WANG Gang, LI Wenlong, JIANG Cheng, et al. Simultaneous calibration of multicoordinates for a dual-robot system by solving the AXB=YCZ problem[J]. IEEE Transactions on Robotics, 2021, 37(4):1172-1185.
[9] 乔贵方,孙大林,宋光明,等.串联机器人标定系统的坐标系快速转换方法[J].机械工程学报, 2020, 56(14):1-8. QIAO Guifang, SUN Dalin, SONG Guangming, et al. A rapid coordinate transformation method for serial robot calibration system[J]. Journal of Mechanical Engineering, 2020, 56(14):1-8.
[10] NUBIOLA A, BONEY I A. Absolute calibration of an ABB IRB 1600 robot using a laser tracker[J]. Robotics and Computer-Integrated Manufacturing, 2013, 29(1):236-245.
[11] MONICA T, GIOVANNI L, NICOLA P. Study of neural-kinematics architectures for model-less calibration of industrial robots[J]. Journal of Robotics and Mechatronics, 2021, 33(1):158-171.
[12] GUO Yixuan, SONG Bao, TANG Xiaoqi, et al. A measurement method for calibrating kinematic parameters of industrial robots with point constraint by a laser displacement sensor[J]. Measurement Science and Technology, 2020, 31(7):1-29.
[13] GUO Kai, PAN Yongping, YU Haoyong. Composite learning robot control with friction compensation:A neural network-based approach[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10):7841-7851.
[14] GUO Kai, PAN Yongping, YU Haoyong. Composite learning control of robotic systems:A least squares modulated approach[J]. Automatica, 2020, 111(10862):1-13.
[15] DU Guanglong, LIANG Yinhao, LI Chunquan, et al. Online robot kinematic calibration using hybrid filter with multiple sensors[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9):7092-7107.
[16] JIANG Yifan, HUANG Xiang, LI Shuanggao. An on-line compensation method of a metrology-integrated robot system for high-precision assembly[J]. Industrial Robot, 2016, 43(6):647-656.
[17] DROLL S. Real time path correction of industrial robots with direct end-effector feedback from a laser tracker[J]. SAE International Journal of Aerospace, 2014, 7(2):222-228.
[18] 史晓佳,张福民,曲兴华,等. KUKA工业机器人位姿测量与在线误差补偿[J].机械工程学报, 2017, 53(8):1-7. SHI Xiaojia, ZHANG Fumin, QU Xinghua, et al. Position and attitude measurement and online errors compensation for KUKA industrial robots[J]. Journal of Mechanical Engineering, 2017, 53(8):1-7.
[19] XIONG Gang, LI Zhoulong, DING Ye, et al. A closed-loop error compensation method for robotic flank milling[J]. Robotics and Computer-Integrated Manufacturing, 2020, 63(1):1-9.
[20] LONANNOU P A, SUN J. Robust adaptive control[M].NJ:Prentice-Hall, 1996.
[21] LIU Qi, HUANG Tian. Inverse kinematics of a 5-axis hybrid robot with non-singular tool path generation[J]. Robotics and Computer-Integrated Manufacturing, 2019, 56(1):140-148.
[22] SLAVKOVIC N, ZIVANOVIC S, KOKOTOVIC B, et al. Simulation of compensated tool path through virtual robot machining model[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, 42(7):374-390.
[23] KHALIL H K. Nonlinear systems[M]. NJ:Prentice Hall, 2002.

基于扰动观测器的工业机器人高精度闭环鲁棒控制

High-precision Closed-loop Robust Control of Industrial Robots Based on Disturbance Observer

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